Care to recommend courses that might be useful for someone intending
to get hands-on with scientific models? What I mean:
I'm a grad student @ UNC; just finished first semester. I want to do a
dual degree in Environmental Sciences and Engineering (ESE) and City
and Regional Planning (CRP). My goal is (eventually) to develop one or
more tools that will help planners to predict the effects of
development decisions on greenhouse-gas (GHG) emissions. As a first
step I'd like to investigate integration of an air-quality (AQ) model
with an LUT (land-use/transportation) model, i.e. one from CRP and one
from ESE. I'm pitching this as a project to a couple of professors
(one on each side), and they seem amenable. The models would likely be
* on the CRP/LUT side, UrbanSim
http://www.urbansim.org/
which is pure python, and looks pretty sweet.
* on the ESE/AQ side, one or more of several older fortran-based
models, probably CMAQ and its ecosystem
http://www.cmaq-model.org/
My background is coding (mostly web apps in perl and eclipse plugins
in java) but I took Chris' bootcamp this summer and have been doing
python in shell since then. My background in scientific computing and
math is pretty weak. As an undergrad (computer science major) I took a
semester course on "scientific computing" that was basically an
introduction to fortran plus coding a few algorithms therein. I'm also
pretty weak in diff eq, having taken only the required 4th semester
math course combining diff eq and linear algebra (in addition to the
usual 3-semester slog through calculus).
So I'm seeking advice regarding courses that would help me get skilled
in integrating, using, and fiddling with scientific models. I'd like
to do as much as possible with python, since numpy, scipy, and the
netCDF libraries seem widely used in this space. (That being said, at
least some fortran seems inevitable.) Feel free to contact me offline,
or to forward this post.
TIA, Tom Roche <Tom_Roche at pobox.com>